Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to: :
Although it is widely recognized that freeze–thaw cycles have a great influence on the properties of asphalt pavement, a quantitative understanding of how freeze–thaw cycles affect cold recycled mixtures with asphalt emulsion (CRME) is so far still lacking. The main objective of the paper was to investigate the performance and microstructure of CRME under freeze–thaw cycles with different water saturation conditions. For this, air voids, high-temperature stability, low-temperature cracking resistance, and moisture susceptibility of CRME were analyzed based on laboratory tests. The micro-morphology and chemical composition of cement asphalt emulsified compound mortar were observed by scanning electron microscopy (SEM). Results showed air voids of CRME increase as freeze–thaw cycles increase; the high-temperature stability, low-temperature cracking resistance, and moisture susceptibility of CRME decrease as freeze–thaw cycles increase; the asphalt strips from the surface of hydration products, and the composite structure mainly consists of hydration products as freeze–thaw cycles increase; the microstructure of CRME is destroyed. The freeze–thaw cycles have a negative effect on the CRME performance and microstructure.
This article examines the mechanical behavior of Basalt fiber-reinforced epoxy (BFRE) and a new type of fiber metal laminates (FMLs) composed of steel, aluminum, and BFRE named as Basalt fiber metal laminate (BFML) under tensile and bending loads. To study the effect of fillers in epoxy, the micro glass powder (MGP) was only added into the epoxy resin in BFRE composites at various volume fractions. It was found that the MGP had no significant effect on tensile strength, but it raised the stiffness and decreased the failure strain of BFRE. On the other hand, bending strength increased by adding MGP. BFML showed superiority in energy absorption via tensile strength. This FML had flexibility much higher than that of BFRE. Adding MGP or metal layer to basalt-reinforced composites improved the mechanical properties in tensile and bending loads. Selective bending specimens of BFRE are studied by SEM to show the positive role of MGP in raising the bending strength and further analysis of the nature of fracture surfaces. High fragmentation of matrix was obvious.
Stern foil is an innovation that can be used on high-speed vessel craft. This innovation uses the same principles as interceptor but using hydrofoil. Interceptor are used to reduce the wetted surface area of the transom by making vortex under the transom, this kind of change will increase speed and reduce the total resistance of the ship. The mechanism on how the stern foil reduces the total resistance is an interesting question in term of ship hydrodynamics. This study aims to analyse the resistance reduction on high-speed patrol vessel by application of stern foil using simulation model. The study was carried out using computational fluid dynamics (CFD) with hydrodynamic parameters using a variation of the angle of attack 3˚ and 0˚ on Froude number range 0.6 - 1.3 with service load at 2 kg. The simulation result was obtained the optimal work for stern foil is at service load (2 kg) is a reduction in the total resistance of about 26,70% with the angle of attack is 0˚ in Froude number 0.9.
The passenger-cargo Roll on/Roll off ship stowage (PRSS) is the core step of passengercargo Roll on/Roll off (RoRo) transportation. The layout of vehicles in the cabin is directly related to the space utilization of the cabin and the efficiency of stowage operations, which in turn affects the economic benefits of the port. In this paper, we address the PRSS problem in the context of passenger-cargo RoRo transportation in the Qiongzhou Strait of China. By focusing on the utilization ratio of the cabin area, the PRSS problem can be viewed as a special version of a two-dimensional knapsack packing (2D-KP) problem with additional constraints, such as two-phase, complex rotation and safe navigation constraints. Then we present a mixed integer linear programming (MILP) mathematical model and an algorithm framework to tackle the PRSS problem. In the algorithm framework, a novel multi-phase heuristic stowage method is proposed to improve the current manual stowage decision-making state which completely depends on operational experience. Finally, several instances are generated based on the realistic date of Qiongzhou Strait to verify the effectiveness of the model and stowage method. Computational results show that the proposed model and stowage method are well suited to solve the PRSS problem and the algorithm framework has a strong robustness in large-scale application experiments.
This paper proposes trajectory tracking algorithm for differential drive type of Automatic Guided Vehicle (AGV) system with the unknown wheel radii using adaptive backstepping control method. To guarantee the tracking errors go to zero, backstepping control method is proposed. By choosing appropriate Lyapunov function based on its kinematic modeling, system stability is guaranteed and a control law can be obtained. In this paper, the unknown radii of left and right wheels caused by uneven load distribution or manufacturing imperfection are considered. To solve this problem, an adaptive law is proposed to estimate the changing of wheels radii. The simulation and experimental results show that the proposed controller successfully estimates the unknown parameters and tracks the reference trajectories.
A super-resolution reconstruction approach based on an improved generative adversarial network is presented to overcome the huge disparities in image quality due to variable equipment and illumination conditions in the image-collecting stage of intelligent pavement detection. The nonlinear network of the generator is first improved, and the Residual Dense Block (RDB) is created to serve as Batch Normalization (BN). The Attention Module is then formed by combining the RDB, Gated Recurrent Unit (GRU), and Conv Layer. Finally, a loss function based on the L1 norm is utilized to replace the original loss function. The experimental findings demonstrate that the self-built pavement crack dataset’s Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) of the reconstructed images reach 29.21 dB and 0.854, respectively. The results improved compared to the Set5, Set14, and BSD100 datasets. Additionally, by employing Faster-RCNN and a Fully Convolutional Network (FCN), the effects of image reconstruction on detection and segmentation are confirmed. The findings indicate that the segmentation results’ F1 is enhanced by 0.012 to 0.737 and the detection results’ confidence is increased by 0.031 to 0.9102 when compared to state-of-the-art methods. It has a significant engineering application value and can successfully increase pavement crack-detecting accuracy.
While driving simulators allow for the examination of a range of driving performance measures in a controlled, relatively realistic and safe driving environment, driver distraction is a multidimensional phenomenon which means that no single driving performance measure can capture all effects of distraction. Furthermore, the large number of driving related outcomes each simulator provides, indicates that the decision regarding which measure or set of measures is used should be guided by specific criteria. The objective of this paper is a comprehensive review of driving performance parameters critical for distracted driving research. For this purpose an extended literature review took place in order to investigate the critical parameters which are examined in the scientific field of driver distraction. Firstly, all driving performance parameters examined in driving simulator experiments are identified and analysed including lateral control, longitudinal control, reaction time, gap acceptance, eye movement and workload measures, while a list of the most common driving simulator dependent variables is cited. Subsequently, a thorough literature review is carried out including 42 studies examining driver distraction through driving simulator experiments which were published in scientific journals, concern recent research and report quantitative results. In this framework, the respective driving performance measures are recorder aiming to investigate which and how they are analysed. A basic remark concerns the quantitative measures used to express driver distraction. In most cases, driver distraction is measured in terms of its impact to driver attention, driver behaviour and driver accident risk. It is noted that the specific measures used vary significantly. However, the diversity in the measures used, in combination with the diversity in the design of the experiments (i.e. road and traffic factors examined, number and duration of trials) often complicates the synthesis of the results, especially for the less commonly examined distraction factors.
This paper analyzes aircraft CO2 emissions (in both quantity and intensity per passenger) during landing and take-off cycles at nine different airports in Jiangsu province (China) over a ten-year time span (2007–2016). Our database is unique and very detailed in that we combine flight schedules, with aircraft type (engines) used, and landing-and-take-off cycles. We are particularly interested in how the spatial characteristics impact emission levels. To this end we estimate a CO2 emission model taking the airport characteristics into account, and apply a spatial classification and autocorrelation model to distinguish between different types of airports and systems. Our analysis shows that: (1) there are strong spatial distribution differences between airports due to the patterns of economic development, airport size and aircraft used; (2) most airports have a high reduction potential of CO2 emission, without a loss of economic performance; (3) significant spatial aggregation effects exist and are persistent during most observational years, which indicates a strong Matthew effect of CO2 emission within Jiangsu province; and (4) airport size, linkage to the local economy, and airport location are closely related to aircraft CO2 emissions. We also provide a number of recommendations to improve airport CO2 emissions and add to sustainable development.
Due to heavy work load of marine diesel engines, the failure in their mechanical components may result in serious accidents. Existing condition monitoring methods for marine diesel engines usually adopt warning after the failure occurred. In order to predict potential faults, this work has put forward a remote intelligent monitoring system for marine diesel engines. The global system for mobile communication mode was employed to construct the basis of data remote transmission, and a new multi-kernel extreme learning machine algorithm was proposed to diagnose the early faults in an intelligent method. Experimental tests were carried out in the marine diesel engine fault diagnosis set-up. The analysis results show that the proposed remote intelligent monitoring system can accurately, timely and reliably detect the potential failures. Meanwhile, the proposed multi-kernel extreme learning machine was compared with the existing methods. The comparison indicates that the multi-kernel extreme learning machine outperforms its rivals in term of fault detection rate by an increase of 3.4%. Therefore, the proposed remote intelligent monitoring system has good prospects for engineering applications.
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.
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